49 citations · 82 across the 6 of their papers we have counts for
6 papers
Accelerating scientific discovery with the common task framework
J. Nathan Kutz, Peter Battaglia, Michael Brenner +12
Machine learning (ML) and artificial intelligence (AI) algorithms are transforming and empowering the characterization and control of dynamic systems in the engineering, physical,…
FC2T2: The Fast Continuous Convolutional Taylor Transform with Applications in Vision and Graphics
Henning Lange, J. Nathan Kutz
Series expansions have been a cornerstone of applied mathematics and engineering for centuries. In this paper, we revisit the Taylor series expansion from a modern Machine Learning…
Deep Probabilistic Koopman: Long-term time-series forecasting under periodic uncertainties
Alex Mallen, Henning Lange, J. Nathan Kutz
Probabilistic forecasting of complex phenomena is paramount to various scientific disciplines and applications. Despite the generality and importance of the problem, general mathem…
Learning to Solve AC Optimal Power Flow by Differentiating through Holomorphic Embeddings
Henning Lange, Bingqing Chen, Mario Berges +1
Alternating current optimal power flow (AC-OPF) is one of the fundamental problems in power systems operation. AC-OPF is traditionally cast as a constrained optimization problem th…
Data-driven Modeling of Rotating Detonation Waves
Ariana Mendible, James Koch, Henning Lange +2
The direct monitoring of a rotating detonation engine (RDE) combustion chamber has enabled the observation of combustion front dynamics that are composed of a number of co- and/or…
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction
Henning Lange, Steven L. Brunton, Nathan Kutz
We propose spectral methods for long-term forecasting of temporal signals stemming from linear and nonlinear quasi-periodic dynamical systems. For linear signals, we introduce an a…